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Ultraviolet/visible Absorbance Trends for Beverages Under Simulated Rinse Conditions and Development of Data-driven Prediction Model by Daniella Saetta,Kristina Buddenhagen,Wenny Noha,Eric Willman,Treavor H. Boyer is a nonfiction available to read on EtoBox.

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Food Control, 146 (2023) 109530. doi:10.1016/j.foodcont.2022.109530 Ultraviolet/visible absorbance trends for beverages under simulated rinse conditions and development of data-driven predict ... 1 1 Introduction 1 2 Materials and methods 2 2.1 Beverages and dilutions 2 2.2 UV/visible absorbance 2 2.3 Determination of taste and odor thresholds 3 2.4 Data analysis and model development 3 2.4.1 Data processing 3 2.4.2 Data analysis via predictive model 3 3 Results and discussion 3 3.1 UV/visible absorbance wavelength scans 3 3.2 UV265 absorbance as function of dilution 5 3.3 UV/visible absorbance and conductivity 5 3.4 UV/visible absorbance and odor/taste thresholds 6 3.5 Prediction of UV/visible absorbance based on user-specified product ingredients 6 3.6 Considerations for industrial application of in-line UV monitoring 7 4 Conclusions 7 Declaration of competing interest 8 CRediT authorship contribution statement 8 Data availability 8 Acknowledgement 8 Appendix A Supplementary data 8 References 8 Conductivity,Machine learning,Optical sensor,Sensory threshold,Water efficiency

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Author

Daniella Saetta,Kristina Buddenhagen,Wenny Noha,Eric Willman,Treavor H. Boyer

Publisher

Elsevier Ltd

Language

EN

Category

nonfiction